Methods for operating a thermal system and corresponding thermal systems as mentioned before are known from prior art. Such thermal systems can be heating and cooling systems.
Heating and cooling systems such as heating, ventilation or air conditioning—normally referred to as HVAC in buildings—are typically controlled by set points. If a control variable exceeds or undercuts the set point, the cooling or heating operation control is applied, often for prolonged periods of time. It is common practice in the state of the art to adjust set points to impact output variables. Additionally, when not adjusting set points, heating and cooling systems can also be de-activated or throttled to reduce energy use when set points are violated. For this, different operational runtime periods might be applied.
Thermal inertia in buildings—important for the overall energetic behaviour of the buildings and playing a crucial role in considerations about thermal storage potential—is less considered as dynamic buildings simulations consume high computation time and require the knowledge of many geometrical and physical parameters, which are often difficult to retrieve from the existing systems. To meet savings or balancing criteria, the use of thermal inertia and its control operation for using its storage potential is essential, see R. Tribalat “Modelling thermal inertia for load prediction purposes”, Centre for environmental policies, Imperial College UK, 2009.
Special purpose facilities like sport centers, factories, etc. drive large-scale components which drive up a substantial amount of energy, which reaches beyond the core HVAC operation of buildings and has a strong link to the facility energy management. In a particular study of grass pitch operation for soccer arena, the management of the thermal properties of the grass field is crucial for business operation itself and at least contributing 50% to the overall energy consumption.
It is common in building systems that there are multiple—independent—control operations possible to reach the same goal via different process paths, e.g. heating rooms via air-conditioning unit versus static heating system, and increase supply temperatures versus applying a lower static temperature for prolonged periods.
In real practice, these different operations are used interchangeable but in static mode based on availability of resources/time. The impact of the control mode, however, depends strongly on the environmental context like climate or ambient temperature, and might not reach business target as requested, e.g. reaching a certain temperature/comfort at a defined time interval. The existing deployments of these control options might consider context for optimizing a specific operation mode and lead the respective input operation parameters. However, there is no measure in place to choose the context-appropriate, most efficient operational control options.
Within the special case of grass pitch heating, the driving point for correct operation is the thermal inertia of the grass pitch. This however is strongly dependent on environmental context which needs an integrated evaluation to choose the right control option to serve the operational goals efficiently. It also very much depends on the operational settings how the impact correlates with the context and the final result.
The main problem considering thermal inertia in control systems is driven by the complexity of the existing models. In case of grass pitch material, the porous media exhibits the thermal characteristics of solid state with very large surface and to some extend fluid intermixing due to different state of irrigation of the ground. Physical models provide high complexity which cannot be applied easily in the daily operation field due to missing detailed physical measurements, see H. A. Dinulescu “An application of irreversible thermodynamics to the problem of heat and moisture migration in soil”, Wärme—und Stoffübertragung, 1980, Volume 13, Issue 1-2, pp 11-25, A. M. Puzrin, G. T. Houslsby “On the Thermodynamics of Porous Continua, Report No. OUEL 2235/01, University of Oxford, UK, and R. Tribalat “Modelling thermal inertia for load prediction purposes”, Centre for environmental policies, Imperial College UK, 2009. Iterative learning control has been applied in the industry in various ways to optimize components and systems, e.g. G. M. Dimitrovski et al. “On Learning Control in industrial furnaces and boilers”, Proceedings of the 15th IEEE International Symposium on Intelligent Control (ISIC 2000), and many others.
Further prior art regarding the operation and control of different systems is disclosed within the following documents: U.S. Pat. No. 8,126,574 B2 is showing a system and a method for dynamic multi-objective optimization of machine selection, integration and utilization. US 2012/0101648 A1 is showing energy-optimal control decisions for systems. WO 2014/089694 A1 shows a self-learning control system and method for optimizing a consumable input variable and WO 2013/039553 A1 shows a load forecasting from individual customer to system level.